An AI-driven social media analytics framework that processes raw platform data to extract sentiment, demographics, trends, and link analysis for users and evaluators.
National Technical Research Organisation (NTRO) · Software
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Decomposed from what the description asks for. Nothing added.
Data Ingestion Pipeline
A multi-platform data collection system that pulls live data, posts, and comments from X, Telegram, Instagram, Facebook, Reddit, and YouTube.
Sentiment Inference
An NLP model that detects nuanced emotions and maps their fluctuation along a timeline.
Demographic Profiling
Models that infer aggregate, anonymized follower demographics based on public profile indicators and bio text.
Trend Detection
A system that automatically identifies, ranks, and predicts rising trends and viral keywords chronologically.
Link Analysis
A network topology mapper that identifies key opinion leaders and visualizes how trends spread among users over time.
Both columns are read off the brief's own wording. Nothing here is inferred from the ministry's name.
Evaluators will check if your framework successfully handles multi-platform data ingestion, specifically requiring X and Telegram while treating Instagram and Facebook as desirable. They will test your NLP models for nuanced emotion detection, demographic inference, trend tracking, and network topology link analysis.
A jury can still ask about these. Decide them deliberately rather than by accident.
Generated from the brief's own wording and the competition's published rules — never from a guess about what this ministry prefers.
Specific APIs or access credentials provided for data collection?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.
Quantitative accuracy targets for sentiment analysis and demographic profiling?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.
Specific visualization requirements for the network topology and link analysis?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.
Has any part of this been shown at a previous event, hackathon or college project?
The guidelines are explicit: your solution must not have appeared in any previous event or programme, of any sort. A recycled project is what a team under time pressure reaches for.
Each one is quoted from a gap in the brief, not a guess about your team.
No dataset provided
The description depends on real data, and the organisers have not attached a dataset link.
What the organisers attached, and what the brief assumes you can get.
Same organisation, same year. Reading two of theirs tells you more about what they care about than reading one.
Pick what you are about to do and copy the prompt. It carries the organisers' own wording, the constraints they never spell out, and an instruction not to invent requirements they never set.
Who has this problem, what already exists, and what you would have to find out.
The brief asks for data ingestion pipeline. How would you build that?
Decoded from SIH26152 itself — A multi-platform data collection system that pulls live data, posts, and comments from X, Telegram, Instagram, Facebook, Reddit, and YouTube. The brief asks for it by name.
The brief asks for sentiment inference. How would you build that?
Decoded from SIH26152 itself — An NLP model that detects nuanced emotions and maps their fluctuation along a timeline. The brief asks for it by name.
The brief asks for demographic profiling. How would you build that?
Decoded from SIH26152 itself — Models that infer aggregate, anonymized follower demographics based on public profile indicators and bio text. The brief asks for it by name.
The brief asks for trend detection. How would you build that?
Decoded from SIH26152 itself — A system that automatically identifies, ranks, and predicts rising trends and viral keywords chronologically. The brief asks for it by name.
The brief asks for link analysis. How would you build that?
Decoded from SIH26152 itself — A network topology mapper that identifies key opinion leaders and visualizes how trends spread among users over time. The brief asks for it by name.
Where does your data come from — a published source, one you collect, or one you generate?
No dataset is attached to this problem statement, so sourcing it is part of the work and nobody told you that.
Why not use what already exists? Name the closest thing to this that is already running.
A team that has not named the alternative themselves is answering this for the first time in the room.
Which single thing will you demonstrate end to end, start to finish, with nothing skipped?
Ours, not a rule: a narrow thing that fully works survives questioning better than a broad thing that half works. If nobody on the team can name it, that is the finding.
Show me this working: evaluators will check if your framework successfully handles multi-platform data ingestion, specifically requiring X and Telegram while treating Instagram and Facebook as desirable.
This is the evaluator read for your problem statement, decoded from the brief's own wording.
Show me this working: they will test your NLP models for nuanced emotion detection, demographic inference, trend tracking, and network topology link analysis.
This is the evaluator read for your problem statement, decoded from the brief's own wording.